1. What is the clearest plain-language description of machine learning?
2. A colleague proposes "using AI to improve customer satisfaction". What is the first question to ask?
3. In a table of past property sales used to predict prices, what makes the sold-price column the "label"?
4. Which statements about training data are correct? Select all that apply.
5. Google Flu Trends over-predicted flu prevalence in 100 of 108 weeks between August 2011 and September 2013. What was the underlying lesson?
6. How do AI, machine learning and deep learning relate to one another?
7. What does the word "deep" refer to in deep learning?
8. What distinguishes a data science project from a machine learning project?
9. Why do large language models produce confidently stated but false information?
10. According to the one-second rule of thumb, which task is most likely automatable with machine learning today?
11. A team builds an accurate model predicting which customers will churn, but churn does not fall. What is the most likely explanation?
12. Which of the following are structural traits of a company genuinely built around AI? Select all that apply.